1. Field of the Invention
The present invention relates to the field of image recognition, and more particularly, to a method of multi-level facial image recognition and a system using such a method to recognize facial image.
2. Description of Related Art
Currently, although there are several recognition techniques available in the facial image recognition, such as feature mode, pattern matching mode, or neural network mode, etc., these techniques can not satisfy the practical requirements. For example, when using the method of feature extraction to recognize the facial image, there are three problems encountered, which are: (1) What are the features required? (2) How many features are necessary? (3) The extraction of the features is not easy.
As to the method of pattern matching, there is a large amount of calculations required for performing a recognition. Therefore, when the image to be matched is large, the time required will be increased greatly. Moreover, it is generally difficult to define a suitable evaluation standard of the matching similarity. In addition, it is also difficult to utilize the automatic learning and recognition capabilities of the neural network for performing facial image recognition due to the complexity and fineness of the variation of the face expression. Thus, a neural network with a simple structure can not be used to analysis and distinguish the difference of facial features. Therefore, it is desirable to provide an improved method and system for recognizing the facial image, so as to mitigate and/or obviate the aforementioned problems.
The object of the present invention is to provide a method of multi-level facial image recognition and a system using the same for reducing the amount of data to be compared in the recognition process thereby greatly increasing the recognition speed.
According to one aspect, the present invention which achieves the object relates to a method of multiple stage face image identification, comprising the steps of: (A) inputting an original image of a face; (B) performing a pre-process to trim the original image into a facial image only containing a complete face image; (C) decomposing the facial image into N resolutions, each having M channels, where Nxe2x89xa72 and Mxe2x89xa72, so that the facial image is decomposed into Nxc3x97M sub-images; (D) in a learning stage, using a front facial image with a normal expression as a learning image; inputting sub-images decomposed from the learning image to Nxc3x97M self-organizing map neural networks, respectively, for performing a non-supervisory classification learning; when the neural networks complete a predetermined learning process, the sub-images of the learning image being input to M neural networks that has completed the learning again, so that each neural network generates a winning unit; and (E) in a testing stage, decomposing a test image thereby starting from the M sub-images having a lowest resolution and inputting the sub-images into the corresponding self-organized map neural networks for generating M winning units; performing a recognition decision process for determining distances from the M winning units to the winning units of each learning image in a corresponding self-organizing map neural network thereby finding possible candidates, and if there is only one candidate, the candidate being a winner and the decision process being completed, while there are more than one candidates, the candidates being retained for performing a decision process in a relative high level of resolution.
According to another aspect, the present invention which achieves the object relates to a multi-level facial image recognition system comprising: means for inputting an original image of a face; means for performing a pre-process to trim the original image into a facial image only containing a complete face image; means for decomposing the facial image into N resolutions, each having M channels, where Nxe2x89xa72 and Mxe2x89xa72, so that the facial image is decomposed into Nxc3x97M sub-images; and a plurality of self-organizing map neural networks, wherein, in a learning stage, a front facial image with a normal expression is used as a learning image; the sub-images decomposed from the learning image are input to Nxc3x97M self-organizing map neural networks, respectively, for performing a non-supervisory classification learning; when the neural networks complete a predetermined learning process, the sub-images of the learning image are input to M neural networks that has completed the learning again, so that each neural network generates a winning unit; and wherein, in a testing stage, a test image is decomposed thereby starting from the M sub-images having a lowest resolution and inputting the sub-images into the corresponding self-organized map neural networks for generating M winning units; a recognition decision process is performed for determining distances from the M winning units to the winning units of each learning image in a corresponding self-organized map neural network thereby finding possible candidates, and if there is only one candidate, the candidate is a winner and the decision process is completed, while there are more than one candidates, the candidates are retained for performing a decision process in a relative high level of resolution.
Other objects, advantages, and novel features of the invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.